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Reimplemented gradients for exponential, seems to work for

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laplace now, needs a visual test though
This commit is contained in:
Alan Saul 2013-10-25 12:21:11 +01:00
parent a46121c430
commit 8ef3625832
5 changed files with 134 additions and 32 deletions
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32
GPy/likelihoods/noise_models/student_t_noise.py
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@ -55,7 +55,7 @@ class StudentT(NoiseDistribution):
:returns: likelihood evaluated for this point
:rtype: float
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
#Careful gamma(big_number) is infinity!
objective = ((np.exp(gammaln((self.v + 1)*0.5) - gammaln(self.v * 0.5))
@ -80,7 +80,7 @@ class StudentT(NoiseDistribution):
:rtype: float
"""
assert np.asarray(link_f).shape == np.asarray(y).shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
objective = (+ gammaln((self.v + 1) * 0.5)
- gammaln(self.v * 0.5)
@ -105,7 +105,7 @@ class StudentT(NoiseDistribution):
:rtype: Nx1 array
"""
assert y.shape == link_f.shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
grad = ((self.v + 1) * e) / (self.v * self.sigma2 + (e**2))
return grad
@ -131,7 +131,7 @@ class StudentT(NoiseDistribution):
Will return diagonal of hessian, since every where else it is 0, as the likelihood factorizes over cases
(the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
"""
assert y.shape == link_f.shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
hess = ((self.v + 1)*(e**2 - self.v*self.sigma2)) / ((self.sigma2*self.v + e**2)**2)
return hess
@ -151,7 +151,7 @@ class StudentT(NoiseDistribution):
:returns: third derivative of likelihood evaluated at points f
:rtype: Nx1 array
"""
assert y.shape == link_f.shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
d3lik_dlink3 = ( -(2*(self.v + 1)*(-e)*(e**2 - 3*self.v*self.sigma2)) /
((e**2 + self.sigma2*self.v)**3)
@ -173,7 +173,7 @@ class StudentT(NoiseDistribution):
:returns: derivative of likelihood evaluated at points f w.r.t variance parameter
:rtype: float
"""
assert y.shape == link_f.shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
dlogpdf_dvar = self.v*(e**2 - self.sigma2)/(2*self.sigma2*(self.sigma2*self.v + e**2))
return np.sum(dlogpdf_dvar)
@ -193,7 +193,7 @@ class StudentT(NoiseDistribution):
:returns: derivative of likelihood evaluated at points f w.r.t variance parameter
:rtype: Nx1 array
"""
assert y.shape == link_f.shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
dlogpdf_dlink_dvar = (self.v*(self.v+1)*(-e))/((self.sigma2*self.v + e**2)**2)
return dlogpdf_dlink_dvar
@ -213,7 +213,7 @@ class StudentT(NoiseDistribution):
:returns: derivative of hessian evaluated at points f and f_j w.r.t variance parameter
:rtype: Nx1 array
"""
assert y.shape == link_f.shape
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
e = y - link_f
d2logpdf_dlink2_dvar = ( (self.v*(self.v+1)*(self.sigma2*self.v - 3*(e**2)))
/ ((self.sigma2*self.v + (e**2))**3)
@ -314,3 +314,19 @@ class StudentT(NoiseDistribution):
p_025 = mu - p
p_975 = mu + p
return mu, np.nan*mu, p_025, p_975
def samples(self, gp):
"""
Returns a set of samples of observations based on a given value of the latent variable.
:param size: number of samples to compute
:param gp: latent variable
"""
orig_shape = gp.shape
gp = gp.flatten()
f = self.gp_link.transf(gp)
#student_t_samples = stats.t.rvs(self.v, loc=f,
#scale=np.sqrt(self.sigma2),
#size=(num_test_points, num_y_samples, num_f_samples))
#Ysim = np.array([np.random.binomial(1,self.gp_link.transf(gpj),size=1) for gpj in gp])
return Ysim.reshape(orig_shape)